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Development of the European Union according to selected demographic indicators
Illeová, Iva ; Fiala, Tomáš (advisor) ; Miskolczi, Martina (referee)
This diploma aims to analyze the development of the European Union Member States for last thirty years in terms of demographic indicators. First chapter is focused on the historical development of the world's population, followed by the development of the European population and political history of the European Union. Next chapter is focused on the most commonly used demographic indicators for international comparisons divided into groups according to demographic processes, their interpretation and calculation. The main part of the work is analysis of selected indicators for the Member States of the EU with regard to the European average of 27 Member States in the period between 1981 and 2011. Another chapter is devoted to modify data for above mentioned simple analysis and cluster analysis, which is located in the last chapter. Cluster analysis was performed using Euclidean distance and using nearest and furthest neighbor and Ward's method. In 1981 the furthest neighbor method divided Member States into five clusters and in 2011 the Ward's method divided Member States again within five clusters, but with a different composition. Original data for Multidimensional description is added to annex with the results of calculations and graphs.
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Unemployment in Czech Republic and EU
Rytíř, Michal ; Arltová, Markéta (advisor) ; Helman, Karel (referee)
Unemployment is a common phenomenon in economy. The unemployment rate is an indicator reflecting the economic situation significantly. Unemployment is followed by the public intensively and that is why it is an important political topic. To fight unemployment it is necessary to analyze its current state, development and estimated future prospects. This thesis is focused on analysis of the state and development of unemployment in the Czech Republic and EU. Its future development is estimated using the Box-Jenkins method.
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Cluster analysis of European Union states using social and economical indicators
Černý, Andrej ; Löster, Tomáš (advisor) ; Bílková, Diana (referee)
Dissertation follows up cluster analysis of European Union states using social and economical indicators. In the first part all the social and economical indicators are defined. In the second part of the dissertation all these indicators in time of economical and financial and later debt crisis in years from 2007 to 2012 in European Union states are analysed. In the third part the cluster analysis is applicated for the social and economical indicators of the European Union states. Five clusters were identified using cluster analysis. Structure of these clusters was changing during years 2007 and 2012. First cluster contained developed Western European countries (Belgium, Denmark, Germany, Ireland, France, Netherlands, Austria, Finland, Sweden and United Kingdom). The second cluster was created from countries, that entered to European Union after year 2004 (Bulgaria, Estonia, Croatia, Latvia, Lithuania, Hungary, Poland, Romania and Slovakia). The third cluster contained Czech Republic, Estonia, Greece, Lithuania, Portugal, Slovakia and Slovenia. In the fourth cluster were Southern European countries Greece, Spain, Italy, Cyprus and Malta and the fifth cluster contained only Luxembourg.
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Cluster analysis of destricts of the Czech Republic ecording to demographic indicators
Saifrtová, Barbora ; Langhamrová, Jitka (advisor) ; Miskolczi, Martina (referee)
Master thesis deals with dividing destricts of the Czech Republic in to clusters acording to demographic indicators during the year 2011. After the theoretical introduction with exploratory data analyiss, factor analysis and cluster analysis is described practical implementation of agglomerative hierarchical clustering. Within the frame cluster analysis we compare results calculated by four methods of clustering, which are the single linkage method, the complete linkage method, the average linkage method and Ward's method. At the conclusion we select the method which divides destricts of the Czech Republic in to the clusters the best. Master thesis includes a prezentations of discovere results with the help of dendrograms and cartograms. The analysis were carried out with the help of the statistical program STATISTICA.
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Comparison of life quality in countries of European Union measured by various indicators
Knoll, Radim ; Malá, Ivana (advisor) ; Bílková, Diana (referee)
In this bachelor thesis I try to describe the quality of life using a wider range of components which affects the quality of life and use these components to compare the quality of life in 28 member countries of the European Union. This work is divided into four parts. The first part is a detailed description of individual components of life quality. Along with that a comparison of all indicators of life quality is shown. Compared indicators were used in multiple calculations and models of life quality. The second part contains an overall comparison of the European Union based on all indicators. The third part consists of a comparison of the values of selected indicators in years of 2007 and 2013. Furthermore cluster analysis which is based on similarity of states classified by chosen indicators is shown. The last part contains a list of individual states ranked by the quality of life in years of 2008 and 2012.
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Evaluation of the Success of Coefficients and Methods Used in Cluster Analysis
Hammerbauer, Jiří ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
The diploma thesis explores with the evaluation of the success of selected indices for determining the number of clusters used in cluster analysis. The aim of this thesis is on the basis of various combinations of clustering methods and distances verify whether, alternatively using which clustering methods and distances is it possible to rely on the results of indices for determining the number of clusters. The results of success rate presented in the third chapter suggest that not all of indices for determining the number of clusters can be used universally. The most successful index is Dunn index, which was able to determine the correct number of clusters in 37 % of cases, respectively Davies-Bouldin index with the share of 70 % when including deviation of one cluster. The success rate is affected by both used method and selected distance.
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Classification of European countries based on their business climate
Pospíchalová, Barbora ; Löster, Tomáš (advisor) ; Pivoňka, Tomáš (referee)
The aim of the thesis is to classify european countries in terms of their business climate using the method of cluster analysis over the years 2008-2013. Changes in classification during this period are associated with events of global significance (e.g. World financial crisis) or local importance (reforms, EU strategy...). Data base consists of indicators describing administrative, financial and law environment for doing business and are publicated by World Bank. Clusters indicate both geographic conditionality and specific attributes of these clusters, which suggest countries with better/worse conditions in some of the areas. Particular attention is given to development in the Czech republic. There was significant change in classificiation between 2008 and 2009 and subsequently became stable. The results of analysis correspond to the existing rankings and indicators of business demography. Potentials for improvement which might leed to stabel economic development according to the conducted analysis are outlined in the end of the thesis (f.e. implementation of unified administrative points, electronization and further simplification of bureaucratic processes).
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The evaluation of coefficients when determining the optimal number of clusters in cluster analysis
Novák, Miroslav ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
The objective of this thesis is the evaluation of selected coefficients of the cluster analysis when determining the optimal number of clusters. The analytical evaluation is performed on 20 independent real datasets. The analysis is made in statistical SYSTAT 13.1 Software. The application of coefficients RMSSTD, CHF, PTS, DB and Dunn's index on real datasets is the main part of this thesis, because the issue of evaluating the results of clustering is not devoted sufficient attention in scientific publications. The main goal is whether the selected coefficients of clustering can be applied in the real situations. The second goal is to compare selected clustering methods and their corresponding metrics when determining the optimal number of clusters. In conclusion, it is found that the optimal number of clusters determined by the coefficients mentioned above cannot be considered to be correct since, after application to the real data, none of the selected coefficients overcome the success rate of 40%, hence, the use of these coefficients in practice is very limited. Based on the practical analysis, the best method in identifying the known number of clusters is the average linkage in connection with the Euclidean distance, while the worst is the Ward's method in connection with the Euclidean distance.
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Evaluating the success of cluster analysis methods
Maršálková, Kateřina ; Löster, Tomáš (advisor) ; Makhalova, Elena (referee)
Cluster analysis is one of the classification methods of multivariate statistical analysis. The task of this analysis is to classify the objects into clusters so that objects inside these clusters are as similar as possible. The aim of this study is to evaluate the success of the classification of objects using six hierarchical cluster analysis methods. To reflect the distance between the objects, are used squared Euclidean and Mahalanobis distances. The success methods are evaluated through the information, which cluster the object belongs to, and this information is already contained in the data files. This thesis pointed out that the Ward's method is one of the most successful hierarchical method in a classification of objects into clusters. This method has been more successful in sorting objects than the other hierarchical methods, both in the case of leaving the correlated variables in the data file as well as removing them. The results of this work show that the highest success of classification objects into clusters is when the data set is cleaned of correlated variables. If the data file is not cleaned, the methods reach better results when the distance between objects is measured by Euclidean metric.
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Analysis of household consumption in the EU
Kolman, Martin ; Bílková, Diana (advisor) ; Malá, Ivana (referee)
The goal of this work is to analyze the evolution of household consumption of the states in the EU. The consumption will be researched in the view of classification COICOP, which is the classification of individual consumption by purpose. After mapping of this evolution the estimation of future values will be done from known time series. This estimation will be performed by two different ways. First one will respect the composition of household consumption in sections of classification COICOP. The second one will only work with time series of average consumption for all sections together. To compare the states cluster analysis will be done. This analysis will be done by two ways again. First one will be aimed to analyze the current situation and the second one will be aimed to analyze the evolution of household consumption. Instead of Microsoft Excel STATGRAPHICS X64 CENTURION and SPSS will be used in this thesis. Household consumption prognosis is the main benefit of this thesis. This prognosis is made for all sections of COICOP. Analysis has shown, that the consumption should rise in future. There are few exceptions, mainly countries with not good economic situation as Greece.
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